The goal of this collaboratory to help the biomedical research community identify collaborators with expertise in artificial intelligence, machine learning, natural language processing, and other related areas. Below is a list of faculty who are seeking new collaborations.
Mary Regina Boland, Ph.D. – artificial intelligence, data mining, machine learning, feature selection
Yong Chen, Ph.D. – machine learning models for dynamic risk prediction, data privacy, distributed algorithms, semi-automated rapid systematic review and meta-analysis
Tessa S. Cook, M.D., Ph.D. – artificial intelligence, machine learning, natural language processing (particularly as applied to radiology reports and the electronic medical record)
John H. Holmes, Ph.D. – machine learning for knowledge discovery, classification and prediction, complex systems, dimensionality reduction, network models, agent-based modeling and simulation, knowledge representation for intelligent systems
Danielle Mowery, Ph.D. – clinical natural language processing, patient phenotyping, machine learning, risk prediction
Dokyoon Kim, Ph.D. – Interpretable deep learning, graph deep learning, machine learning on graphs, feature selection
Despina Kontos, Ph.D. – machine learning, pattern recognition, medical image analysis, radiomics, radiogenomics, imaging biomarkers, integrated diagnostics
Jason H. Moore, Ph.D. – artificial intelligence, automated machine learning, feature engineering, feature selection, genetic programming
Marylyn Ritchie, Ph.D. – supervised machine learning, unsupervised phenotype clustering, feature selection, evolutionary algorithms
Li Shen, Ph.D. – machine learning, biomedical informatics, medical image computing, network science, visual analytics, shape analysis, big data science in biomedicine
Ryan J. Urbanowicz, Ph.D. – artificial intelligence, interpretable machine learning, feature selection, data mining pipelines, rule-based evolutionary algorithms
